forest_cpbayes: Forest plot presenting pleiotropy result obtained by CPBayes.

Description

Run the forest_cpbayes function to create a forest plot that presents the pleiotropy result obtained by cpbayes_uncor or cpbayes_cor.

Usage

forest_cpbayes(mcmc_output, level = 0.05, PPAj_cutoff = 0.01)

Arguments

mcmc_output

A list returned by either cpbayes_uncor or cpbayes_cor. This list contains all the primary results and MCMC data produced by cpbayes_uncor or cpbayes_cor. No default is specified. See the example below.

level

A numeric value. (1-level)% confidence interval of the unknown true genetic effect (beta/log(odds ratio)) on each trait is plotted in the forest plot. Default choice is 0.05.

PPAj_cutoff

A numeric value. It's a user-specified threshold of PPAj (trait-specific posterior probability of association). Only those traits having PPAj values above this cut-off are included in the forest plot. So, the choice of this variable as '0.0' includes all traits in the forest plot. Default is 0.01.

Value

The output produced by this function is a diagram file in .pdf format. The details of the diagram are as follows:

file_name

The pdf file is named after the genetic variant. So, if the argument `Variant' in cpbayes_uncor or cpbayes_cor is specified as 'rs1234', the figure file is named as rs1234.pdf.

Column1

First column in the figure specifies the name of the phenotypes.

Column2

Second column provides the trait-specific univariate association p-value for a trait.

Column3

Third column provides the trait-specific posterior probability of association (PPAj) produced by CPBayes.

Column4

Fourth column states whether a phenotype was selected in the optimal subset of associated/non-null traits detected by CPBayes. If a phenotype was not selected, selected and positively associated, selected and negatively associated, its association status is stated as null, positive and negative, respectively.

Column5

In the right section of the figure, the primary eatimate and confidence interval of the beta/log odds ratio parameter for a trait is plotted.

References

Majumdar A, Haldar T, Bhattacharya S, Witte JS (2018) An efficient Bayesian meta analysis approach for studying cross-phenotype genetic associations. PLoS Genet 14(2): e1007139.

Examples

Run this code

# NOT RUN {
data(ExampleDataUncor)
BetaHat <- ExampleDataUncor$BetaHat
SE <- ExampleDataUncor$SE
traitNames <- paste("Disease", 1:10, sep = "")
SNP1 <- "rs1234"
result <- cpbayes_uncor(BetaHat, SE, Phenotypes = traitNames, Variant = SNP1)
# }
# NOT RUN {
forest_cpbayes(result, level = 0.05)
# }
# NOT RUN {
# }